chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
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+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using QuantConnect.Data;
+using QuantConnect.Data.Market;
+using QuantConnect.Interfaces;
+using System;
+using System.Collections.Generic;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Demonstration of payments for cash dividends in backtesting. When data normalization mode is set
+    /// to "Raw" the dividends are paid as cash directly into your portfolio.
+    /// </summary>
+    /// <meta name="tag" content="using data" />
+    /// <meta name="tag" content="data event handlers" />
+    /// <meta name="tag" content="dividend event" />
+    public class DividendRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private decimal _sumOfDividends;
+        private Symbol _symbol;
+
+        /// <summary>
+        /// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
+        /// </summary>
+        public override void Initialize()
+        {
+            SetStartDate(1998, 01, 01);  //Set Start Date
+            SetEndDate(2006, 01, 01);    //Set End Date
+            SetCash(100000);             //Set Strategy Cash
+            // Find more symbols here: http://quantconnect.com/data
+            _symbol = AddEquity("SPY", Resolution.Daily,
+                dataNormalizationMode: DataNormalizationMode.Raw).Symbol;
+        }
+
+        /// <summary>
+        /// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
+        /// </summary>
+        /// <param name="data">TradeBars IDictionary object with your stock data</param>
+        public override void OnData(Slice slice)
+        {
+            if (Portfolio.Invested) return;
+            SetHoldings(_symbol, .5);
+        }
+
+        /// <summary>
+        /// Raises the data event.
+        /// </summary>
+        /// <param name="dividends">Data.</param>
+        public override void OnDividends(Dividends dividends) // update this to Dividends dictionary
+        {
+            var dividend = dividends[_symbol];
+            var holdings = Portfolio[_symbol];
+            Debug($"{dividend.Time.ToStringInvariant("o")} >> DIVIDEND >> {dividend.Symbol} - " +
+                $"{dividend.Distribution.ToStringInvariant("C")} - {Portfolio.Cash} - " +
+                $"{holdings.Price.ToStringInvariant("C")}"
+            );
+            _sumOfDividends += dividend.Distribution * holdings.Quantity;
+        }
+        
+        public override void OnEndOfAlgorithm()
+        {
+            // The expected value refers to sum of dividend payments
+            if (Portfolio.TotalProfit != _sumOfDividends)
+            {
+                throw new RegressionTestException($"Total Profit: Expected {_sumOfDividends}. Actual {Portfolio.TotalProfit}");
+            }
+
+            var expectNetProfit = _sumOfDividends - Portfolio.TotalFees;
+            if (Portfolio.TotalNetProfit != expectNetProfit)
+            {
+                throw new RegressionTestException($"Total Net Profit: Expected {expectNetProfit}. Actual {Portfolio.TotalNetProfit}");
+            }
+
+            if (Portfolio[_symbol].TotalDividends != _sumOfDividends)
+            {
+                throw new RegressionTestException($"{_symbol} Total Dividends: Expected {_sumOfDividends}. Actual {Portfolio[_symbol].TotalDividends}");
+            }
+        }
+
+        /// <summary>
+        /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
+        /// </summary>
+        public bool CanRunLocally { get; } = true;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate which languages this algorithm is written in.
+        /// </summary>
+        public List<Language> Languages { get; } = new() { Language.CSharp };
+
+        /// <summary>
+        /// Data Points count of all timeslices of algorithm
+        /// </summary>
+        public long DataPoints => 16077;
+
+        /// <summary>
+        /// Data Points count of the algorithm history
+        /// </summary>
+        public int AlgorithmHistoryDataPoints => 0;
+
+        /// <summary>
+        /// Final status of the algorithm
+        /// </summary>
+        public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
+
+        /// <summary>
+        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+        /// </summary>
+        public Dictionary<string, string> ExpectedStatistics => new()
+        {
+            {"Total Orders", "1"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "2.354%"},
+            {"Drawdown", "28.200%"},
+            {"Expectancy", "0"},
+            {"Start Equity", "100000"},
+            {"End Equity", "120462.08"},
+            {"Net Profit", "20.462%"},
+            {"Sharpe Ratio", "-0.063"},
+            {"Sortino Ratio", "-0.078"},
+            {"Probabilistic Sharpe Ratio", "0.015%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "-0.016"},
+            {"Beta", "0.521"},
+            {"Annual Standard Deviation", "0.083"},
+            {"Annual Variance", "0.007"},
+            {"Information Ratio", "-0.328"},
+            {"Tracking Error", "0.076"},
+            {"Treynor Ratio", "-0.01"},
+            {"Total Fees", "$2.56"},
+            {"Estimated Strategy Capacity", "$36000000.00"},
+            {"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
+            {"Portfolio Turnover", "0.02%"},
+            {"Drawdown Recovery", "126"},
+            {"OrderListHash", "8068ff5f4917787e48d90fda94de340c"}
+        };
+    }
+}